A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction |
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Authors: | Safa Meraghni Labib Sadek Terrissa Meiling Yue Jian Ma Samir Jemei Noureddine Zerhouni |
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Affiliation: | 1. LINFI Laboratory, University of Biskra, Algeria;2. FEMTO-ST Institute (UMR CNRS 6174), Univ. Bourgogne Franche-Comte, France;3. FCLAB Research Federation (FR CNRS 3539), Univ. Bourgogne Franche-Comte, France;4. School of Reliability and Systems Engineering, Beihang University, China;5. National Key Laboratory of Science and Technology on Reliability and Environmental Engineering, China |
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Abstract: | Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data. |
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Keywords: | Deep learning Digital twin Prognostics Proton exchange membrane fuel cell Remaining useful life |
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